The output of S-multi-SNE, \(Y \in \mathbb {R}^{N \times d}\) , are low-dimensional embeddings of all samples, including the ones in the test set. These projections can then be treated as input features into classification algorithms. In a practical manner, any general-purpose classifier can be used to predict the classes of the unlabelled samples. In this paper, several standard classifiers were explored: (a) Support Vector Machine (SVM) [1]}, (b) Linear Discriminant Analysis (LDA) [2]}, (c) Decision Trees (DT) [3]}, (d) Random Forests (RF) [4]}, (e) Neural Network, via Multi-Layer Perceptron (NN) [5]}, and (f) K-Nearest Neighbours (KNN) [6]}.
